Firebolt cloud data warehouse is built from the ground-up to deliver an order-of-magnitude performance leap. Firebolt delivers high concurrency, sub-second latency analytics and is deployed as a replacement or side-by-side with Redshift or Snowflake. Firebolt is built for the modern data stack with speed, efficiency and SQL simplicity in mind.
Redshift was originally built as a shared nothing architecture with tightly coupled storage and compute. It has since been retrofitted with de-coupled storage and compute with RA3 nodes only.
Firebolt SaaS data warehouse was designed from the ground up to provide the fastest data experiences on top of a de-coupled storage and compute architecture.
Driving low latency analytics requires fast access to granular data. Redshift’s coarse data access is typically not suitable for low latency analytics. Redshift lacks specialized indexes to speed up access.
Firebolt leverages highly efficient “F3” file format which is sorted, indexed and compressed. Specialized Indexes reduce data access down to granular data ranges, providing order of magnitude performance improvement enabling sub-second analytics.
Redshift has concurrency limitation of 50 (15 recommended) per cluster. Concurrency scaling requires workarounds to go beyond these limits and can result in additional costs.
A single Firebolt engine supports hundreds of concurrent queries, eliminating the need for additional engines and associated costs. Efficient query processing and data pruning result in high concurrency plus infrastructure efficiency.
Redshift provides the ability to consume resources in different forms including provisioned clusters, serverless, Redshift Spectrum, concurrency scaling and others. This can introduce variability and unpredictability, especially as you scale.
Firebolt provides simple transparent pricing that includes two elements: compute consumed and optimized object storage. Compute can be scaled to zero or shaped specific to workloads in a granular form.
Redshift offers compute with only 5 current generation node types with fixed vCPU to Memory ratio, thus limiting provisioning granularity. Choice of node types limits scalability.
Firebolt engines can be provisioned from over 30 instance types to address diverse workloads in a cost effective manner. This provides various combinations of vCPU and Memory ratios. Offers granular scale-up and scale-out options. Additionally, Firebolt provides easy workload isolation and segmentation.
Redshift leverages distribution keys and sort keys to distribute and sort data. Reclamation of deleted space and maintenance of sorted data require expensive operations in the form of vacuum and analyze.
Firebolt offers specialized indexes designed to optimize granular data access and to process joins efficiently. Primary indexes and Join indexes are maintained transparently.
Redshift provides materialized views with auto refresh and query rewrite capabilities. However, auto refresh of materialized views may not be immediate.
Firebolt’s aggregating indexes are defined once, incrementally auto-synced at ingest and data can be queried at index speed immediately without rewriting queries.
Explorium provides an external data acquisition and management platform to help companies make better business decisions by integrating data across many data sources. Explorium evaluated Firebolt and Redshift for their analytics needs. Explorium’s evaluation demonstrated that Firebolt could deliver significantly lower query response times for their workloads. For more information on how Explorium migrated to Firebolt, please see the detailed case study.
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